Machine Learning For Beginners Guide Algorithms (eBook)

Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction

William Sullivan (Autor)

eBook Download: EPUB

2017
268 Seiten
Publishdrive (Verlag)
978-1-9756-3232-8 (ISBN)

Infos

Machines can LEARN ?!?!
Machine learning occurs primarily through the use of ' algorithms' and other elaborate procedures
Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning
Note: Bonus chapters included inside!
Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself
Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available for beginners.
What You'll Learn

• Supervised Learning

• Unsupervised Learning

• Reinforced Learning

• Algorithms

• Decision Tree

• Random Forest

• Neural Networks

• Python

• Deep Learning

• And much, much more!

This is the most comprehensive and easy to read step by step guide in machine learning that exists.
Learn from one of the most reliable programmers alive and expert in the field
You do not want to miss out on this incredible offer!

---

Machines can LEARN ?!?! Machine learning occurs primarily through the use of "e; algorithms"e; and other elaborate procedures Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning Note: Bonus chapters included inside! Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead? Its a worthwhile read and the most valuable investment you can make for yourself Other books easily retail for $50-$100+ and have far less quality content. This book is by far superior and exceeds any other book available for beginners. What You'll Learn Supervised Learning Unsupervised Learning Reinforced Learning Algorithms Decision Tree Random Forest Neural Networks Python Deep Learning And much, much more! This is the most comprehensive and easy to read step by step guide in machine learning that exists. Learn from one of the most reliable programmers alive and expert in the field You do not want to miss out on this incredible offer!

Textauszug

Congratulations on purchasing/downloading this book !

The following chapters we’ll discuss are going to inform you of everything that you need to know in order to analyze data. Whenever you are analyzing data, you are going to be taking data and modifying it, inspecting it, creating models of the data, and even cleaning up the data so that it can be used in the equations that you need to use it in.

Data analytics is used in the real world in a lot of jobs that you may be looking to get yourself into. In order to get yourself into data analytics and get hired into a job that will pay you more money, you are going to want to ensure that you have everything that you need to know under your belt so that you are placing yourself one step ahead of the competition and get yourself hired!

When you are doing data analysis, you are going to be taking raw data that you gathered from your sources and turning it into information that you are going to find more useful not only to you, but to others too. All of the data that you collect is going to come from you answering a question or testing a hypothesis or even when you are trying to disprove a theory.

You are going to have to keep in mind that there are going to be multiple phases that you are going to go through in order to work with the data that you collect so that it can be analyzed correctly. You should not be surprised if one of the phases means that there is going to be extra work on your end in previous steps so that you are getting the appropriate data analysis.

At the point in time that data is placed into an analysis program, you are going to have to go on the parameters that are set into place either by you or the person who is making the decisions to make sure that you are making sure that your consumer is getting an excellent product.

Most of the data that you are going to be analyzing is going to be collected through an innovative process such as going to a particular area in a neighborhood and collecting information from that specific community. From there, the variables are going to be broken down based on the individuals who fit into groups such as age, income, so on and so forth. Your data is going to categorical or numerical depending on what you are trying to figure out.

As you go about collecting your data, you are going to need to follow all of the requirements that are going to keep you on track for the study. In other words, you are not going to want to ask about someone’s favorite movie when you are trying to discover how many children they have. There are going to be sensors that you are going to use such as traffic cameras or environmental sensors that are going to give you all of the data that you want to know. Not only that, but you can do an interview face to face in order to obtain the data that you need.

Once you have collected all of the data that you need, then you are going to be at the step where you are going to be ready to share the data that you have analyzed. In the end, the result is going to require that you get feedback from your consumer so that you can help make sure that your company is running more efficiently so that you can ensure that you are getting the product out to the customer that they are wanting. In other words, you are going to be making sure that you are not putting out a defective product that no one is going to buy.

There are going to be times that you are going to have to use visualization tools to make sure that you are sharing the data in a way that everyone understands. Not everyone is going to be able to understand the data by being told a variety of number.

There are plenty of books on this subject on the market, thanks again for choosing this one! Every effort was made to ensure it is full of as much useful information as possible, please enjoy!

Description

Data analytics is used in the real world in a lot of jobs that you may be looking to get yourself into. In order to get yourself into data analytics and get hired into a job that will pay you more money, you are going to want to ensure that you have everything that you need to know under your belt so that you are placing yourself one step ahead of the competition and get yourself hired!

The chapter discussions of this book are going to inform you of everything that you need to know in order to analyze data.

These include :

●  Things you need to know about regression analysis and social network analysis

●  What are big data, data and text mining, and web scraping and their applications in the real world

●  Techniques in data analysis

●  Learn how to reduce your workload through data reduction

●  Risks that you have to know in order to protect yourself, your company, and your data

●  And so much more...

Chapter 1: Regression Analysis

When dealing with statistical models, you are going to use regression analysis. Regression analysis is a process that you are going to be determining what relationships there are amongst all of the variables that are being used. Using techniques such as modeling and analyzing the variables will be included whenever the focus is on the relationship that falls between the independent and the dependent variables.

In other words, regression analysis is going to assist the user in understanding that how the usual value for the dependent variable is going to change whenever one of the independent variables remains unchanged while other independent variables are changed. Estimating the conditional expectation for the dependent variable depending on what the independent variable is one of the most common regression analysis.

The less common use is going to be on quantile or the location parameter for the distribution of the dependent variable given what independent variables are being used. Nonetheless, all cases when regression analysis is used is going to be to target the function of the independent variables.

Regression models

There are three variables that all regression models will have.

Your dependent variable will be y.

The independent variable will be x.

Finally, the unknown parameters are going to be denoted with the beta symbol. Which is going to be represented as the scalar or the vector.

There are going to be a lot of different fields for applications which are going to consist of various terms that are going to be used to describe the independent and dependent variables.

Regression models are going to relate the dependent variable to the function of the independent variable and the unknown parameters.

In other words, it is going to be formalized as

This is done in order to carry out the regression analysis. In each expression, f has to be specified. There are going to be times that this form is going to be based on the information that you gathered about the connection between x and y while it does not depend on the data that you have obtained. If there is no knowledge available, there is going to be a convenient or flexible form that f is going to be chosen for.

You are going to assume now that the vector for your parameters of beta is going to be the length of k. So, the user is going to have to provide the information for the dependent variable to be able to get a regression analysis.

In the event that the n data points for y and x are observed, then n is going to be less than k in most of the classical approaches that you are going to see in regression analysis and if this happens, then there will be no performed analysis. This happens because the equations’ system is going to define the regressions model for the undetermined. There is also not going to be enough data for you to be able to recover beta.

If n is equal to the k data points, then the function of f will end up being linear. Your equation is going to be which is going to be able to able to be solved exactly rather than give you an approximation. Therefore, you are going to be reducing your work because you are only going to be solving the n set of equations in which n’s unknowns are going to be the elements of beta. As long as x is linearly independent, there will always be a unique solution. Therefore, the solution is not going to exist or there are going to be multiple solutions in case f will be nonlinear.

Another common situation you are going to see is when n is greater than the k data points that are being observed. If you find yourself working with this situation then there is going to be enough information in your hands to be able to estimate what the value of beta is going to best fit the data that you are working with. So, your regression model is going to be viewed as an over determined system inside of beta.

When working with the last regression analysis that was mentioned you are going to have the tools provided to you so that you can find a solution for your parameters of beta. Not only that, but under some statistical assumptions, the analysis is going to use the extra data that you have so that you can predict what your dependent variable is.

Independent measurements

When looking at regression models, there are going to be three unknown parameters. If you look at an experiment where there are ten measurements that are all the same value for the independent variable vector X, there is going to be that many independent variables. So, for this example, there are going to be three independent variables.

In this instance, the regression...

Produkt-Details

• Erscheint lt. Verlag: 19.8.2017
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Informatik ► Theorie / Studium ► Algorithmen
• Schlagworte: algorithms • Deep learning • Java • machine learning • Machine Learning & Data Anayltics & Data Science • Neural networks • Python • Random Forest & Artificial Intelligence • Scikit-Learn Intelligent Systems • supervised learning • Unsupervised Learning
• ISBN-10: 1-9756-3232-X / 197563232X
• ISBN-13: 978-1-9756-3232-8 / 9781975632328